Practice Project

import cv2
import numpy as np

def process_image(input_path, output_path):
    """
    Complete image processing pipeline using Module 2 skills.
    Loads an image, inspects it, modifies it, and saves the result.
    """
    # --- STEP 1: Load the image ---
    image = cv2.imread(input_path)
    if image is None:
        print(f"Error: Could not load '{input_path}'")
        return False

    print("=== Image Processing Pipeline ===")
    print(f"Input: {input_path}")

    # --- STEP 2: Inspect dimensions ---
    h, w, c = image.shape
    print(f"Dimensions: {w}x{h}, {c} channels")
    print(f"Memory: {image.nbytes:,} bytes")

    # --- STEP 3: Analyse pixel values ---
    b, g, r = cv2.split(image)
    print(f"Blue  mean: {b.mean():.1f}")
    print(f"Green mean: {g.mean():.1f}")
    print(f"Red   mean: {r.mean():.1f}")

    # --- STEP 4: Add a colour border ---
    border_size = 20
    result = image.copy()
    # Top and bottom borders (green)
    result[:border_size, :] = [0, 255, 0]
    result[-border_size:, :] = [0, 255, 0]
    # Left and right borders (green)
    result[:, :border_size] = [0, 255, 0]
    result[:, -border_size:] = [0, 255, 0]

    # --- STEP 5: Add a watermark box ---
    # Draw a semi-dark overlay in bottom-right corner
    overlay = result.copy()
    cv2.rectangle(overlay, (w-120, h-40), (w, h), (0, 0, 0), -1)
    cv2.addWeighted(overlay, 0.5, result, 0.5, 0, result)

    # --- STEP 6: Save both colour and grayscale versions ---
    cv2.imwrite(output_path, result)
    gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
    gray_path = output_path.replace(".", "_gray.")
    cv2.imwrite(gray_path, gray)

    print(f"Saved: {output_path}")
    print(f"Saved: {gray_path}")
    print("=== Pipeline complete ===")
    return True

# Run the pipeline
process_image("your_photo.jpg", "processed_output.jpg")